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Harish Mahavar

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Data security is rapidly gaining importance as the volume of data companies collect, analyze and monetize grows exponentially. New data processing tools and platforms are emerging at an increasing rate, as are the ways in which an organization consumes data. In this presentation Mukund Sarma and Feni Chawla talk about the unique technical and cultural challenges of running a data security program and share some practical solutions that have worked well at our company. These slides were presented at the BSides Seattle 2024 conference.

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Welcome to the April edition of WIPAC Monthly, the magazine brought to you by Water Industry Process Automation & Control. In this month's edition, along with the latest news from the industry we have articles on: The use of artificial intelligence and self-service platforms to improve water sustainability A feature article on measuring wastewater spills An article on the National Underground Asset Register Have a good month, Oliver

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GAS POWER CYCLES Cycles: Otto, Diesel, Dual, Brayton - Calculation of mean effective pressure - Air standard efficiency - Comparison of cycles INTERNAL COMBUSTION ENGINES Classification - Components and their function - valve timing diagram and port timing diagram - actual and theoretical p-v diagram of two stroke and four stroke engines – carburettor - diesel pump and injector system - battery and magneto ignition system - principles of combustion and detonation in CI engines - lubrication and cooling systems - performance parameters and calculations.

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N-Grade deals with the maintenance of university, department, faculty, student information within the university. N-Grade is an automation system, which is used to store the department, faculty, student, courses and information of a university. Starting from registration of a new student in the university, it maintains all the details regarding the attendance and marks of the students. The project deals with retrieval of information through an INTRANET based campus wide portal. It collects related information from all the departments of an organization and maintains files, which are used to generate reports in various forms to measure individual and overall performance of the students.

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African Journal of Biological Sciences is an International peer-reviewed, Open Access journal that publishes original research articles as well as review articles in all areas of Biological Sciences. It operates a fully open access publishing model which allows open global access to its published content. This model is supported through Article Processing Charges. For more information on Article Processing charges click here. Its scope embraces Animal Sciences, Biochemistry, Bioinformatics, Biotechnology, Botany, Cell Biology, Developmental Biology, Ecology, Environmental Sciences, Ethno Medicine, Food Science, Freshwater Biology, Genetics, Immunology, Marine Biology, Microbiology, Molecular Biology, Physiology, Plant Sciences, Structural Biology,Toxicology,Zoology etc. It is essential that authors prepare their manuscripts according to established specifications. Failure to follow them may result in papers being delayed or rejected. Therefore, contributors are strongly encouraged to read the author guidelines carefully before preparing a manuscript for submission. The manuscripts should be checked carefully for grammatical, punctuation errors. All papers are subjected to peer review. All articles published in this journal represent the opinion of the authors and not reflect the official policy of the Journal of African Journal of Biological Sciences

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Structural Analysis and Design of Foundations: A Comprehensive Handbook for Students and Professionals. Unlock the potential of foundation design with Dr. Costas Sachpazis’s enlightening handbook, a meticulously crafted guide poised to become an indispensable resource for both budding and seasoned civil engineers. This comprehensive manual illuminates the theoretical and practical aspects of structural analysis and design across various types of foundations and retaining walls. Within these pages, Dr. Sachpazis distills complex engineering principles into digestible, step-by-step processes, enhanced by detailed diagrams, case studies, and real-world examples that bridge the gap between academic study and professional application. From soil mechanics and load calculations to innovative design techniques and sustainability considerations, this book covers a vast landscape of structural engineering. Key Features: • In-Depth Analysis and Design: Explore thorough explanations of both shallow and deep foundation designs, supported by case studies that demonstrate their practical implementations. • Practical Guides: Benefit from detailed guides on site investigation, bearing capacity calculations, and settlement analysis, ensuring designs are both robust and reliable. • Innovative Techniques: Discover the latest advancements in foundation technology and retaining wall design, preparing you for future trends in civil engineering. • Educational Tools: Utilize this handbook as an educational tool, perfect for both classroom learning and professional development. Whether you're a student eager to learn the fundamentals or a professional seeking to deepen your expertise, Dr. Sachpazis’s handbook is designed to support and inspire excellence in the field of structural engineering. Embrace this opportunity to enhance your skills and contribute to building safer, more efficient structures.

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LIST OF EXPERIMENTS: 1. Implement simple vector addition in Tensor Flow. 2. Implement a regression model in Keras. 3. Implement a perception in TensorFlow/Keras Environment. 4. Implement a Feed Forward Network in TensorFlow/Keras. 5. Implement an image classifier using CNN in TensorFlow/Keras. 6. Improve the deep Learning model by fine tuning hyper parameters. 7. Implement a Transfer Learning concept in image classification. 8. Using a pre trained model on Keras for transfer learning. 9. Perform Sentimental Analysis using RNN. 10. Implement an LSTM based Auto encoding inTensorflow/Keras. 11. Image generation using GAN. ADDITIONAL EXPERIMENTS 12. Train a deep Learning model to classify a given image using pre trained model. 13. Recommendation system from sales data using Deep Learning. 14. Implement Object detection using CNN. 15. Implement any simple Reinforcement Algorithm for an NLP problem.

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